Really great question, and one that I find that most people don't really understand on an intuitive level.
AUC is in fact often preferred over accuracy for binary classification for a number of different reasons. First though, let's talk about exactly what
AUC is. Honestly, for being one of the most widely used efficacy metrics, it's surprisingly obtuse to figure out exactly how
AUC stands for
Area Under the Curve, which curve you ask? Well, that would be the
ROC stands for Receiver Operating Characteristic, which is actually slightly non-intuitive. The implicit goal of
AUC is to deal with situations where you have a very skewed sample distribution, and don't want to overfit to a single class.
A great example is in spam detection. Generally, spam datasets are STRONGLY biased towards ham, or not-spam. If your data set is 90% ham, you can get a pretty damn good accuracy by just saying that every single email is ham, which is obviously something that indicates a non-ideal classifier. Let's start with a couple of metrics that are a little more useful for us, specifically the true positive rate (
TPR) and the false positive rate (
Now in this graph,
TPR is specifically the ratio of true positive to all positives, and
FPR is the ratio of false positives to all negatives. (Keep in mind, this is only for binary classification.) On a graph like this, it should be pretty straightforward to figure out that a prediction of all 0's or all 1's will result in the points of
(1,1) respectively. If you draw a line through these lines you get something like this:
Which looks basically like a diagonal line (it is), and by some easy geometry, you can see that the
AUC of such a model would be
0.5 (height and base are both 1). Similarly, if you predict a random assortment of 0's and 1's, let's say 90% 1's, you could get the point
(0.9, 0.9), which again falls along that diagonal line.
Now comes the interesting part. What if we weren't only predicting 0's and 1's? What if instead, we wanted to say that, theoretically we were going to set a cutoff, above which every result was a 1, and below which every result were a 0. This would mean that at the extremes you get the original situation where you have all 0's and all 1's (at a cutoff of 0 and 1 respectively), but also a series of intermediate states that fall within the
1x1 graph that contains your
ROC. In practice you get something like this:
So basically, what you're actually getting when you do an
AUC over accuracy is something that will strongly discourage people going for models that are representative, but not discriminative, as this will only actually select for models that achieve false positive and true positive rates that are significantly above random chance, which is not guaranteed for accuracy.